Mera M Eugenia, Morán Manuel
Departamento de Fundamentos del Análisis Económico I, Universidad Complutense, 28223 Madrid, Spain.
Chaos. 2006 Mar;16(1):013116. doi: 10.1063/1.2151159.
We propose an algorithm for the reduction of observational noise in chaotic multivariate time series. The algorithm is based on a maximum likelihood criterion, and its goal is to reduce the mean distance of the points of the cleaned time series to the attractor. We give evidence of the convergence of the empirical measure associated with the cleaned time series to the underlying invariant measure, implying the possibility to predict the long run behavior of the true dynamics.
我们提出了一种用于减少混沌多元时间序列中观测噪声的算法。该算法基于最大似然准则，其目标是减少清理后的时间序列的点到吸引子的平均距离。我们给出了与清理后的时间序列相关的经验测度收敛到潜在不变测度的证据，这意味着有可能预测真实动力学的长期行为。
